The phenomenon of bright spots and shadows appearing in images taken by a camera of a subject under one or more lights or other illuminators is well known. Their presence may be considered to be a mere inconvenience or as rendering an image unacceptable for its intended purpose. Consequently, professional photographers and video camera operators are acutely aware of the light sources that are present in a scene. In many situations, such as in a photography studio, it is possible to position the subject and arrange the lighting to eliminate bright spots or glare and to minimize shadows. This problem becomes more difficult in a television studio where the subject moves rather than remains still or even remains in a specific location. Even where bright spots can be eliminated by camera and lighting positions the process of positioning can be quite time consuming. In addition, if a person is required to stay at a particular location that person may appear to be nervous or otherwise uncomfortable. Hence, the control of lighting and camera positions often does not solve the problem.
Camera images are used in a variety of locations to identify a subject whose picture has been taken. These situations range from the identification of people for security or surveillance to the identification of products and product defects in automated manufacturing lines. Bright spots often occur if a person is wearing glasses or reflective clothing and if a product has a highly reflective surface or is contained in a glass or clear plastic package. The presence of a bright spot in an image may make it impossible to identify the person, product or product defect from the image. Hence, there is a need for a method and apparatus for eliminating bright spots from images.
The art has developed a number of processes for removing artifacts such as bright spots from images. These techniques range from airbrushing to digitizing the image and then applying one or more algorithms to the image. Some techniques use two or more images which are combined. Many of these prior art methods are quite time consuming taking several minutes or even hours to complete. Some prior art methods require computer hardware having large memory capacities which can be quite expensive. Thus, there is a preference for image processing that can be done rapidly using less memory and less expensive computer hardware.
There are several methods known as biometrics for recognizing or identifying an individual from personal biological characteristics. Some of these methods involve imaging of the face or eye and analyzing the facial features, retinal vascular patterns of the eye, or patterns in the iris of the eye. In recent years there has been a demand for more reliable systems to rapidly identify individuals, particularly those persons who desire access to a secured area or system. A common example of such a secured system are automated teller machines which allow authorized users to conduct banking transactions. Many of these systems are used by a wide variety of people. Very often these people demand quick as well as accurate identification. U.S. Pat. No. 5,717,512 to Chmielewski et al. discloses a compact system for rapidly obtaining images of the eye of a user of an automated teller machine. These images are then used to identify the user based upon patterns in the user's iris.
A technique for accurately identifying individuals using iris recognition is described in U.S. Pat. No. 4,641,349 to Flom et al. and in U.S. Pat. No. 5,291,560 to Daugman. The systems described in these references require clear, well-focused images of the eye. The presence of eyeglasses tends to interfere with good eye images because of reflections on the eyeglasses. Contact lenses may also cause reflections that interfere with eye imaging. However, because contact lenses have a greater curvature than eyeglasses reflections from contact lenses are smaller and less of a problem than reflections from eyeglasses.
Reflections may come from the system's own illumination. In this case, calculations show that the irradiance (illuminance for visible light) at the camera lens from the specular reflection of an illuminator from eyeglasses is on the order of 1000 times greater than the irradiance at the camera of the image of the eye caused by diffuse reflection of the illuminator. A camera viewing the eye must have a combination of lens, aperture, and exposure time that will result in a sufficiently bright image of the eye. Thus, the much brighter specular reflection of the illuminator will saturate the picture elements (pixels) of the camera's image sensor that cover the area of the specular reflection, and all information about the portion of an eye image obscured by this reflection will be lost.
It is possible to ask the subject to remove his or her eyeglasses in order to get a good image of the subject's eye. However, this is potentially annoying, and the subject may refuse to remove the glasses, or avoid using the system. Consequently, there is a need for an imaging system that can obtain useful images of the eye while minimizing the effect of bright spots, often called specular reflections, caused by the system's own illumination without requiring the subject to remove any eyeglasses or contact lenses that may be present.
Since specular reflection of illumination on eyeglasses depends on the geometric arrangement of illumination with respect to the eyeglasses and an imaging camera, one could use multiple light sources with relatively wide spacing from one another, and turn off one or more of the light sources which cause specular reflections on the eyeglasses that obscure the camera's view of the iris. Yet, these techniques will not eliminate all specularities in images of all subjects using a system because the subjects change while for practical reasons the positions of the lighting and camera must remain fixed or can be varied very little. Nevertheless, the same physical arrangement of camera and illuminators may be used as a platform for a method of image fusion for removing the negative effects of these specular reflections.
In general, image fusion involves three steps: (1) acquiring two or more images of the same scene such that good data for each point in the scene may be obtained from one of the images, (2) a method to detect good data at each point, and (3) a method to merge the data from the images into a single image. Two or more images of the same scene may be created by using different sources and angles of illumination for each image, and one approach for finding good data and fusing it into a single image is multi-resolution image processing, also called pyramid processing.
In "A VLSI Pyramid Chip for Multiresolution Image Analysis" by Van der Wal and Burt (International Journal of Computer Vision, Vol. 8 No. 3, 1992, pp. 177-189), multiple types of pyramid processing of images are briefly but precisely described. In particular, the Laplacian pyramid is defined. As described in detail in "The Laplacian Pyramid as a Compact Image Code" by Burt and Adelson (IEEE Transactions on Communications, Vol. COM-31, No. 4, April 1983, pp. 532-540), the Laplacian pyramid represents an image as a set of bandpass components. The Laplacian pyramid representation of an image enables examination and filtering of various spatial frequency bands, and also the composition of a single image from spatial frequency components selected from multiple images.
Several United States patents show the use of the Laplacian pyramid and related multi-resolution image processing to achieve various objectives. In U.S. Pat. No. 4,661,986, for "Depth-of-focus Imaging Process Method", Adelson teaches a method of using pyramid processing to synthesize an optimally focused image from multiple images. U.S. Pat. Nos. 5,325,449 and 5,488,674, both titled "Method for Fusing Images and Apparatus Therefor", to Burt et al. teach the use of pyramid processing and directionally-sensitive spatial frequency filters to form a composite image with extended information content. "The Noise Reduction System" of Adelson et al in U.S. Pat. No. 5,526,446 uses multi-resolution image processing to filter noise from images. These methods are directed to the hardware and procedures used to process images without concern as to how the images are obtained. They tend to require expensive hardware and can be relatively slow.